A Compute-Efficient Algorithm for Robust Eyebrow Detection
In this paper, we propose a novel method to robustly identify and classify arrow markings in road images. In the proposed method, simple and unique signatures are first derived for the various arrow types, based on signed edge maps and decomposing the arrows into smaller parts. The signed edge maps are processed using Hough Transform (HT), and the resulting Hough spaces are analyzed systematically, using a set of simple rules. The signatures are rotation-invariant and scale-invariant, thereby making the approach robust to variations in the appearance of the arrow markings. It is shown that the method yields a high detection and classification accuracy, of as high as 97% in the test images considered.